基于磁场刺激的肌电信号模式识别的研究

对于人体表面肌电（SEMG）信号提出一种新的研究方法，即在磁场刺激下，采用小波变换的方法，对从掌长肌、肱桡肌、尺侧腕屈肌和肱二头肌上采集的4路表面肌电信号进行分析，并提取其6级小波分解系数绝对值累加和的平均值作为信号的特征．构建特征矢量．输入神经网络分类器进行模式识别，经过训练能够成功地识别出握举、展拳、腕内旋、腕外旋、屈腕、伸腕、前臂内旋、前臂外旋8种运动模式．实验结果表明，该方法识别率高，所需数据量少．运算速度快，实时性好，为肌电等生物电信号的研究提供了一种新方法．     

人手运动状态的识别

提出了一种新的模式分类方法,该分类法采用小波变换和李雅普诺夫指数构造特征矢量,利用Elman神经网络在非线性建模方面的优势,构建前馈神经网络,以此进行特征分类。通过对前臂伸肌、屈肌以及旋前肌采集的肌电信号的处理分析,有效地实现了对握拳、展拳、手腕内旋和手腕外旋4种动作模式的识别。结果表明该分类器有较高的识别准确率和更稳定的再现性。     

基于小波变换的表面肌电信号的情感识别

情感识别是情感计算的一个关键问题。针对表面肌电信号（EMG）的非平稳性,采用小波变换方法对表面肌电信号进行分析,提取小波系数最大值和最小值构造特征矢量输入用L-M算法改进的BP神经网络分类器进行情感状态识别。实验表明,用表面肌电信号对joy、anger、sadness、pleasure 4种情感识别效果较好。也说明用小波变换方法提取特征,用神经网络作分类器的方法用于情感识别有很大的应用前景。     

基于小波包与支持向量机的复杂信号模式识别

为很好地识别神经和肌肉的功能状态,针对表面肌电信号的非平稳特性,提出了采用小波包变换的方法对原始肌电信号进行分解,并提取其频段系数的最大奇异值构建特征矢量.利用"一对一"的分类策略和二又树构建多类支持向量机分类器,经训练后可以有效地对前臂8种动作的表面肌电信号进行识别,8种动作的平均识别率达到99.375%.实验结果表明,支持向量机分类器的识别率明显优于传统的BP神经网络、Elman神经网络和RBF神经网络分类器,且鲁棒性好,并具有良好的泛化推广能力.     

基于小波包变换的肌电信号特征提取

本文提出一种新的基于小波包变换的特征提取方法,提取表面肌电信号进行小波包变换后得到的信号的协方差矩阵的特征值的最大值作为特征值。利用该方法对表面肌电信号提取特征值构建特征矢量,送入Elman神经网络对手部6种动作模式进行识别,在Matlab平台上进行实验仿真。实验结果表明,该方法取得了很好的识别效果。     

基于小波变换的多特征融合sEMG模式识别

针对单一特征值表征能力差的情况,根据小波变换的多分辨分析思想,采用基于多种母小波的多特征融合的特征提取方法对表面肌电信号进行特征提取。本实验对十名测试人员进行肌电信号的采集,对日常生活中的四个基本下肢动作进行测试。首先,分别基于DB、Dmey和Bior三种不同的母小波,采用离散小波变换通过不同的分析方法对表面肌电信号进行多尺度分解。然后,通过分析发现,不同肌肉在不同特征提取方式下表征效果存在差异,为了结合不同特征方式的特点对基于不同小波基的特征值进行融合分析并比较。最后,将特征值分别输入到Elman神经网络和BP神经网络进行模式识别并比较分析。实验结果表明：通过对不同特征值进行识别比较,融合处理的特征值可以达到98.7%的识别率,并且,BP神经网络相较于Elman神经网络识别效果更好。     

基于WPT和LDA的表面肌电信号特征识别方法

针对时域特征参数在表面肌电信号(SEMG)模式识别过程中的局限性,提出一种小波包变换(WPT)和线性判别分析(LDA)相结合的新方法;通过虚拟仪器采集桡侧腕屈肌和肱桡肌两路表面肌电信号,应用小波包变换对表面肌电信号进行多尺度分解,提取小波包系数并计算其均方根作为特征参数,应用线性判别分析对表面肌电信号数据进行分类识别;实验结果表明,采用此方法成功地从表面肌电信号中识别握拳、展拳、手腕内翻和手腕外翻4种动作,与时域参数相比,此方法更能有效提取表面肌电信号信息,且有较高的动作识别率,识别率高达98.2%。     

基于多通道sEMG小波包分解特征的人手动作模式识别方法

为了满足主动康复训练和人机交互等复杂应用场景对多样性的人手运动模式识别需求,提出了一种基于多通道表面肌电信号sEMG小波包分解特征的人手动作模式识别方法。通过实验对比分析,确定了最佳采样布局方案,通过采集前臂表面肌电信号,设计了基于数字滤波器的肌电信号活动段自动标识算法,能快速准确完成样本动作标签的制作。以原始肌电信号的小波包分解系数作为特征向量训练分类器。通过对比不同隐含层节点数对分类器模式识别准确率的影响,最终确定BP神经网络模式分类器的所有结构参数。设计并训练完成了BP神经网络人手运动模式分类器。对9种手部运动的平均识别率达到93.6%,计算时间小于150ms。     

基于小波变换与神经网络的表面肌电信号的情感识别

情感识别是情感计算的一个关键问题。针对表面肌电图(EMG)的非平稳性,采用小波变换方法对表面肌电信号进行分析,提取小波系数最大值和最小值构造特征矢量,分别输入用L-M算法改进的BP神经网络分类器和最近邻法分类器进行情感识别。实验表明,提取EMG的小波系数对joy、anger、sadness、pleasure四种情感进行识别,BP神经网络分类器识别效果优于最近邻法分类器。说明小波变换的方法对EMG进行分析是可行且有效的,并有很大的应用前景。     

基于DSP的人体手势动作实时识别系统

针对人体表面肌电信号(SEMG)的非平稳性、小波包变换系数维数过高和识别率低的问题,设计了基于DSP处理器TMS320VC5502硬件平台的便携式人体手势动作实时识别系统,并提出了一种小波包主元分析(WPPCA)和线性判别分析(LDA)相结合的表面肌电信号动作特征识别新方法。实验结果表明,该方法能够将小波包系数矩阵由16维降到4维,并且对前臂的握拳、展拳、手腕内翻和手腕外翻4种动作模式的平均正确识别率达99.5%,与传统的小波包变换相比有较高的识别率。     

An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.     

运用神经网络对音频数据索引的最优基的选择

在详细探讨了反向传播训练算法之后，提出了用神经网络选择音频数据索引最优基的方法．该方法用小波变换抽取音频信号的关键系数，根据四层小波包二分树确定输出神经元的数量与含义，用Levenberg—Marquardt修正反向传播算法构造与训练了一个32—8—8人工神经网络．试验表明，可以用该神经网络代替复杂的代价函数方法来选择音频数据索引的最优基．     

Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network

In this paper, a condition monitoring and faults identification technique for rotating machineries using wavelet transform and artificial neural network is described. Most of the conventional techniques for condition monitoring and fault diagnosis in rotating machinery are based chiefly on analyzing the difference of vibration signal amplitude in the time domain or frequency spectrum. Unfortunately, in some applications, the vibration signal may not be available and the performance is limited. However, the sound emission signal serves as a promising alternative to the fault diagnosis system. In the present study, the sound emission of gear-set is used to evaluate the proposed fault diagnosis technique. In the experimental work, a continuous wavelet transform technique combined with a feature selection of energy spectrum is proposed for analyzing fault signals in a gear-set platform. The artificial neural network techniques both using probability neural network and conventional back-propagation network are compared in the system. The experimental results pointed out the sound emission can be used to monitor the condition of the gear-set platform and the proposed system achieved a fault recognition rate of 98% in the experimental gear-set platform.     

Probabilistic neural networks combined with wavelet coefficients for analysis of electroencephalogram signals

Abstract: In this paper, the probabilistic neural network is presented for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction by wavelet transform and classification using the classifiers trained on the extracted features. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrates that the wavelet coefficients obtained by the wavelet transform are features which represent the EEG signals well. The conclusions indicate that the probabilistic neural network trained on the wavelet coefficients achieves high classification accuracies (the total classification accuracy is 97.63%).     

轴承故障融合诊断方法

针对传统人工神经网络在故障诊断中应用的局限性 ,提出一种基于小波变换、遗传算法与神经网络的融合故障诊断方法。该方法先用小波变换对原始采样信号进行特征提取 ,再用遗传算法优化选择最为重要的特征作为神经网络的输入参数。最后 ,由神经网络进行状态识别和特征分类。这样不仅减少网络训练时间 ,降低网络计算量 ,而且有效提高分类的准确性及故障诊断的可靠性。轴承故障诊断实验结果表明 ,该方法是有效的。     

Classification of EEG signals using neural network and logistic regression

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.     

Combined neural network model to compute wavelet coefficients

Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients.     

基于小波神经网络的头部检测技术

文章所述头部检测方法,首先采用Mallat小波分解提取的头部特征,作为神经网络的输入,通过构造训练集对BP网络进行训练,自举训练方法提高了神经网络的泛化能力。在此基础上,通过在整个图象上自左至右、自上至下移动窗口,用训练好的神经网络对待识模式进行识别,最后根据启发式规则进行后处理。该算法已经应用于头部跟踪项目中,取得了较好的效果。     

基于小波系数聚类的特征提取分类方法

神经网络是一种普遍采用的模式分类方法,当对样本的抽样数目较大时,神经网络结构复杂,训练时间激增,分类性能下降,针对这一问题,提出一种基于快速小波变换特征提取的分类方法。着先对婆婆以系数矩阵的每行进行聚类,表达重要频率范围内小波系数矩阵的行有较多的聚类数,从而大大减少了神经网络的输入数,而同时保留了有用的信息。特征提取后,采用小波系数的能量值特征量,应用径向基函数网络识别肺发出的各种不同的声音,实验证明：该方法有较高的识别率。